A Novel Combination Method for On-line Process Monitoring and Fault Diagnosis

Abstract

on-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been successfully as a multivariate statistical process control tool for detecting faults in processes with highly correlated variables. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-MSVMs method performs good diagnosis capability and overall diagnosis correctness rate.